Selection of non-small-cell lung cancer patients for treatment with monoclonal antibody drugs targeting EGFR pathway

ABSTRACT

Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer (NSCLC) patient is likely to benefit from a monoclonal antibody drug targeting an epidermal growth factor receptor pathway. A mass spectrum is obtained from a sample (e.g. blood sample) from the patient. One or more predefined pre-processing steps are performed on the mass spectrum. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained after the pre-processing steps have been performed. Such values are used in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other patients to identify the patient as being likely to benefit from treatment with the drug.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of our prior U.S. patentapplication Ser. No. 11/396,328 filed Mar. 31, 2006, currently pending,published as U.S. patent publication No. 2007/0231921. The entirecontent of the '121 patent application publication is incorporated byreference herein.

BACKGROUND

This invention relates to the field of identifying cancer patients asbeing likely to benefit from treatment with drugs targeting theepidermal growth factor receptor (EGFR) pathway. The identification forinitial selection for treatment involves mass spectral analysis of bloodsamples from the patient in conjunction with a classification algorithmusing a training set of class-labeled spectra from other patients withthe disease.

Non-Small-Cell Lung Cancer (NSCLC) is a leading cause of death fromcancer in both men and women in the United States. There are at leastfour (4) distinct types of NSCLC, including adenocarcinoma, squamouscell, large cell, and bronchioalveolar carcinoma. Squamous cell(epidermoid) carcinoma of the lung is a microscopic type of cancer mostfrequently related to smoking. Adenocarcinoma of the lung accounts forover 50% of all lung cancer cases in the U.S. This cancer is more commonin women and is still the most frequent type seen in non-smokers. Largecell carcinoma, especially those with neuroendocrine features, iscommonly associated with spread of tumors to the brain. When NSCLCenters the blood stream, it can spread to distant sites such as theliver, bones, brain, and other places in the lung.

Treatment of NSCLC has been relatively poor over the years.Chemotherapy, the mainstay treatment of advanced cancers, is onlymarginally effective, with the exception of localized cancers. Whilesurgery is the most potentially curative therapeutic option for NSCLC,it is not always possible depending on the stage of the cancer.

Recent approaches for developing anti-cancer drugs to treat the NSCLCpatients focus on reducing or eliminating the ability for cancer cellsto grow and divide. These anti-cancer drugs are used to disrupt thesignals to the cells to tell them whether to grow or die. Normally, cellgrowth is tightly controlled by the signals that the cells receive. Incancer, however, this signaling goes wrong and the cells continue togrow and divide in an uncontrollable fashion, thereby forming a tumor.One of these signaling pathways begins when a chemical in the body,called epidermal growth factor, binds to a receptor that is found on thesurface of many cells in the body. The receptor, known as the epidermalgrowth factor receptor (EGFR) sends signals to the cells, through theactivation of an enzyme called tyrosine kinase (TK) that is found withinthe cells. The signals are used to notify cells to grow and divide.

The use of targeted therapies in oncology has opened new opportunitiesto improve treatment options in advanced stage solid tumors wherechemotherapy was previously the only viable option. For example, drugstargeting the epidermal growth factor receptor (EGFR) pathway (includingwithout limitation, Tarceva (erlotinib), Erbitux (cetuximab), Iressa(gefitinib)) have been approved or are in evaluation for treatment ofadvanced stage solid tumors in particular non-small-cell lung cancer(NSCLC). Metro G et al, Rev Recent Clin Trials. 2006 January; 1(1):1-13.

One limitation of nearly all systemic cancer therapies is that a singleagent will be active in only a minority of patients. As the field oftargeted therapies evolves, it is becoming apparent that predictivebiomarkers are integral to the success of any given therapy. In fact,many agents that have been recently approved by the regulatoryauthorities have been in diseases that harbor a universal molecularalteration, and thus a de facto predictive marker (e.g. imatinib inchronic myelogenous leukemia), or in conjunction with an assay to selectpatients (e.g. trastuzumab in HER2 positive breast cancer). By the sametoken, administering a targeted agent to an unselected patientpopulation is usually accompanied by a modest to nonexistent responserate (e.g. gefitinib 250 mg in HNSCC). Ostensibly the successfuldevelopment of any drug should be linked to predictors of its efficacyas these markers would markedly increase the likelihood that anindividual patient will benefit. Given the morbidity and burden oftreating cancer patients with ineffective agents, it is imperative thatthese endeavors are undertaken.

While in some trials EGFR-inhibitors (EGFR-I) have been shown togenerate sufficient survival benefit even in unselected populations, inothers there was no substantial benefit. This lead AstraZeneca towithdraw their EGFR-tyrosine kinase inhibitor (TKI) (gefitinib, Iressa)from the United States market. Even in the case of approved EGFR-Is ithas become more and more clear that efficient and reliable tests arenecessary to identify those patients that might benefit from treatmentwith EGFR-Is vs. those that are not likely to benefit. Ladanyi M, etal., Mod Pathol. 2008 May; 21 Suppl 2:S16-22.

In our prior U.S. patent application Ser. No. 11/396,328, published asU.S. patent publication No. 2007/0231921, we have shown that a simpleserum-based pre-treatment test using mass spectrometry and sophisticateddata analysis techniques using a classifier and a training set ofclass-labeled spectra from other patients with the disease has promisefor patient selection for treatment with drugs targeting the EGFRpathway in non-small cell lung cancer patients. See also Taguchi F. etal, JNCI 2007 v 99(11), 838-846, the content of which is incorporated byreference herein. The test, called VeriStrat in its commercial version,assigns the label “VeriStrat good” or “VeriStrat poor” to pre-treatmentserum or plasma samples. It has been shown in the JNCI paper that“VeriStrat good” patients are more likely to benefit from EGFR-Itreatment than VeriStrat poor patients with a hazard ratio of “VeriStratgood” vs. “VeriStrat poor” patients of approximately 0.5.

SUMMARY OF THE INVENTION

We have discovered that the methods of mass spectral analysis of patientsamples and classification using a training set described in our priorpatent application provide not only a selection tool for initiallyidentifying NSCLC patients as being likely to benefit from smallmolecule drugs targeting the EGFR pathway, such as gefitinib anderlotinib, but also that the methods provide a selection tool forselection of NSCLC patients for treatment with monoclonal antibody EGRFinhibitors. Thus, we have determined that the selection for NSCLCpatients for treatment applies to the two main classes of EGFRinhibitors, namely (1) small molecule TKIs such as gefitinib anderlotinib, and (2) monoclonal antibody EGFR inhibitors such as cetuximab(Erbitux) and panitumumab.

Additionally, as the methods of this disclosure require only simpleblood samples, the methods enable a fast and non-intrusive way ofselection of such patients.

In one specific embodiment, a method is disclosed of determining whethera NSCLC patient is likely to benefit from treatment with a monoclonalantibody drug targeting the EGFR pathway (e.g., Erbitux (cetuximab),panitumumab or equivalent) comprising the steps of:

a) obtaining a mass spectrum from a sample from the patient;

b) performing one or more predefined pre-processing steps on the massspectrum obtained in step a);

c) obtaining values of selected features in said spectrum at one or morepredefined m/z ranges after the pre-processing steps on the massspectrum in step b) have been performed;

d) using the values obtained in step c) in a classification algorithmusing a training set comprising class-labeled spectra produced fromsamples from other patients to identify the patient as likely to benefitwith treatment with the said drug.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing a method for selection of NSCLC patientsfor treatment With EGFR inhibitors in accordance with a preferredembodiment of this invention.

FIG. 2 is a Kaplan-Meier plot for a set of NSCLC patients treated withcetuximab showing the class label for such patients using the method ofFIG. 1. The patients labeled “good” had a better prognosis followingtreatment with cetuximab than the patients labeled “poor” with a hazardratio of 0.43 (95% CI:0.14-1.30) of good versus poor.

DETAILED DESCRIPTION

We have examined the MS profiles from serum or plasma samples fromrecurrent and/or metastatic NSCLC patients who were treated withcetuximab as monotherapy. The MALDI mass spectra were obtained from eachsample and each patient was classified into “good” or “poor” outcomegroups for survival comparison. We have found that the MS profile waspredictive of survival outcomes in all EGFR-I-treated cohorts.

The methods for selection of NSCLC patients for treatment withmonoclonal antibody EGFR-I is illustrated in flow chart form in FIG. 1as a process 100.

At step 102, a serum or plasma sample is obtained from the patient. Inone embodiment, the serum samples are separated into three aliquots andthe mass spectroscopy and subsequent steps 104, 106 (including sub-steps108, 110 and 112), 114, 116 and 118 are performed independently on eachof the aliquots. The number of aliquots can vary, for example there maybe 4 5 or 10 aliquots, and each aliquot is subject to the subsequentprocessing steps.

At step 104, the sample (aliquot) is subject to mass spectroscopy. Apreferred method of mass spectroscopy is matrix assisted laserdesorption ionization (MALDI) time of flight (TOF) mass spectroscopy,but other methods are possible. Mass spectroscopy produces data pointsthat represent intensity values at a multitude of mass/charge (m/z)values, as is conventional in the art. In one example embodiment, thesamples are thawed and centrifuged at 1500 rpm for five minutes at fourdegrees Celsius. Further, the serum samples may be diluted 1:10, or 1:5,in MilliQ water. Diluted samples may be spotted in randomly allocatedpositions on a MALDI plate in triplicate (i.e., on three different MALDItargets). After 0.75 ul of diluted serum is spotted on a MALDI plate,0.75 ul of 35 mg/ml sinapinic acid (in 50% acetonitrile and 0.1%trifluoroacetic acid (TFA)) may be added and mixed by pipetting up anddown five times. Plates may be allowed to dry at room temperature. Itshould be understood that other techniques and procedures may beutilized for preparing and processing serum in accordance with theprinciples of the present invention.

Mass spectra may be acquired for positive ions in linear mode using aVoyager DE-PRO or DE-STR MALDI TOF mass spectrometer with automated ormanual collection of the spectra. Seventy five or one hundred spectraare collected from seven or five positions within each MALDI spot inorder to generate an average of 525 or 500 spectra for each serumspecimen. Spectra are externally calibrated using a mixture of proteinstandards (Insulin (bovine), thioredoxin (E. coli), and Apomyglobin(equine)).

At step 106, the spectra obtained in step 104 are subject to one or morepre-defined pre-processing steps. The pre-processing steps 106 areimplemented in a general purpose computer using software instructionsthat operate on the mass spectral data obtained in step 104. Thepre-processing steps 106 include background subtraction (step 108),normalization (step 110) and alignment (step 112). The step ofbackground subtraction preferably involves generating a robust,asymmetrical estimate of background in the spectrum and subtracts thebackground from the spectrum. Step 108 uses the background subtractiontechniques described in U.S. published applications 2007/0231921 andU.S. 2005/0267689, which are incorporated by reference herein. Thenormalization step 110 involves a normalization of the backgroundsubtracted spectrum. The normalization can take the form of a partialion current normalization, or a total ion current normalization, asdescribed in our prior patent application U.S. 2007/0231921. Step 112aligns the normalized, background subtracted spectrum to a predefinedmass scale, as described in U.S. 2007/0231921, which can be obtainedfrom investigation of the training set used by the classifier.

Once the pre-processing steps 106 are performed, the process 100proceeds to step 114 of obtaining values of selected features (peaks) inthe spectrum over predefined m/z ranges. Using the peak-width settingsof a peak finding algorithm, the normalized and background subtractedamplitudes may be integrated over these m/z ranges and assigned thisintegrated value (i.e., the area under the curve between the width ofthe feature) to a feature. For spectra where no peak has been detectedwithin this m/z range, the integration range may be defined as theinterval around the average m/z position of this feature with a widthcorresponding to the peak width at the current m/z position. This stepis also disclosed in further detail in our prior patent application U.S.2007/0231921.

At step 114, as described in our patent application published as US2007/0231921, the integrated values of features in the spectrum isobtained at one or more of the following m/z ranges:

5732 to 5795

5811 to 5875

6398 to 6469

11376 to 11515

11459 to 11599

11614 to 11756

11687 to 11831

11830 to 11976

12375 to 12529

23183 to 23525

23279 to 23622 and

65902 to 67502.

In a preferred embodiment, values are obtained at least eight of thesem/z ranges, and more preferably at all 12 of these ranges. Thesignificance, and methods of discovery of these peaks, is explained inthe prior patent application publication U.S. 2007/0231921.

At step 116, the values obtained at step 114 are supplied to aclassifier, which in the illustrated embodiment is a K-nearest neighbor(KNN) classifier. The classifier makes use of a training set of classlabeled spectra from a multitude of other patients (NSCLC cancerpatients). The application of the KNN classification algorithm to thevalues at 114 and the training set is explained in our patentapplication publication U.S. 2007/0231921. Other classifiers can beused, including a probabilistic KNN classifier or other classifier.

At step 118, the classifier produces a label for the spectrum, either“good”, “poor” or “undefined”. As mentioned above, steps 104-118 areperformed in parallel on the three separate aliquots from a givenpatient sample (or whatever number of aliquots are used). At step 120, acheck is made to determine whether all the aliquots produce the sameclass label. If not, an undefined result is returned as indicated atstep 122. If all aliquots produce the same label, the label is reportedas indicated at step 124.

If the label reported at step 124 is “good” it indicates that thepatient is likely to benefit from administration of the EGFR pathwaytargeting drug (monoclonal antibody EGFR-I), or continued administrationin the case of monitoring a patient in the course of treatment. If thelabel reported at step 124 is “poor” it indicates that the patient isnot likely to benefit from administration of the EGFR-I.

It will be understood that steps 106, 114, 116 and 118 are typicallyperformed in a programmed general purpose computer using software codingthe pre-processing step 106, the obtaining of spectral values in step114, the application of the K-NN classification algorithm in step 116and the generation of the class label in step 118. The training set ofclass labeled spectra used in step 116 is stored in memory in thecomputer or in a memory accessible to the computer.

The methods described in this application have been applied to a set of17 samples from NSCLC patients that were collected before treatment withcetuximab (tradename Erbitux, Imclone). Of these 8 yielded the label“good” and 9 yielded the label “poor”. The analysis was performed in afully blinded manner, i.e. no clinical data were available during thedetermination of the label. Once the labels were generated the clinicaldata were unblinded and a Kaplan-Meier analysis for overall survivalcould be performed from the clinical data for the endpoint overallsurvival. The Kaplan-Meier curves are shown in FIG. 2 for the patientslabeled “good” and “poor”. The patients labeled “good” had a betterprognosis following treatment with cetuximab than the patients labeled“poor” with a hazard ratio of 0.43 (95% CI:0.14-1.30) of good versuspoor. The good and poor curves are close to statistically significantlydifferent with a log-rank p-value of 0.065. These results indicate thatthe test described in this application can be used to separate NSCLCpatients into groups with statistically different prognosis followingtreatment with cetuximab.

From the above discussion, it will be appreciated that we have describeda method of determining whether a NSCLC patient is likely to benefitfrom treatment with a monoclonal antibody drug targeting the EGFRpathway, comprising the steps of:

a) obtaining a mass spectrum from a sample from the patient;

b) performing one or more predefined preprocessing steps on the massspectrum obtained in step a);

c) obtaining values of selected features in said spectrum at one or morepredefined m/z ranges after the pre-processing steps on the massspectrum in step b) have been performed; and

d) using the values obtained in step c) in a classification algorithmusing a training set comprising class-labeled spectra produced fromsamples from other patients to identify the patient as being likely tobenefit from treatment with the said drug.

In preferred embodiments, the one or more m/z ranges comprises one ormore m/z ranges selected from the group of m/z ranges consisting of:

5732 to 5795

5811 to 5875

6398 to 6469

11376 to 11515

11459 to 11599

11614 to 11756

11687 to 11831

11830 to 11976

12375 to 12529

23183 to 23525

23279 to 23622 and

65902 to 67502.

Preferably but not necessarily, the mass spectrum is obtained from aMALDI mass spectrometer.

Variations from the particular details of the preferred embodimentsdisclosed are of course possible without departure from the scope of theinvention. All questions of scope are to be determined by reference tothe appended claims.

1. A method of determining whether a non-small cell lung cancer (NSCLC)patient is likely to benefit from treatment with a monoclonal antibodydrug targeting the EGFR pathway, comprising the steps of: a) obtaining amass spectrum from a blood-based sample from the NSCLC patient; b)performing one or more predefined pre-processing steps on the massspectrum obtained in step a); c) obtaining integrated intensity valuesof selected features in said spectrum at one or more predefined m/zranges after the pre-processing steps on the mass spectrum in step b)have been performed; and d) using the values obtained in step c) in aclassification algorithm using a training set comprising class-labeledspectra produced from blood-based samples from other cancer patients toidentify the NSCLC patient as being either likely or not likely tobenefit from treatment with the said monoclonal antibody drug targetingthe EGFR pathway; wherein the said monoclonal antibody drug targetingthe EGFR pathway is selected from the group of drugs consisting ofcetuximab or the equivalent and panitumumab or the equivalent.
 2. Themethod of claim 1, wherein the one or more m/z ranges comprises one ormore m/z ranges selected from the group of m/z ranges consisting of:5732 to 5795 5811 to 5875 6398 to 6469 11376 to 11515 11459 to 1159911614 to 11756 11687 to 11831 11830 to 11976 12375 to 12529 23183 to23525 23279 to 23622 and 65902 to
 67502. 3. The method of claim 1,wherein the mass spectrum is obtained from a MALDI mass spectrometer. 4.The method of claim 1, wherein the predefined pre-processing stepscomprise a background subtraction step producing a background-subtractedspectrum, and a normalization step performing a normalization of thebackground-subtracted spectrum.